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<a href="_regularization_network_trainer_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Trainer for a Regularization Network or a Gaussian Process</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * </span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * </span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \author      T. Glasmachers</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * \date        2007-2012</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> *</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * </span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * </span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * </span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * </span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> *</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment"> */</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span> </div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_REGULARIZATIONNETWORKTRAINER_H</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="preprocessor">#define SHARK_ALGORITHMS_REGULARIZATIONNETWORKTRAINER_H</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span> </div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span> </div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_svm_trainer_8h.html">shark/Algorithms/Trainers/AbstractSvmTrainer.h</a>&gt;</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="preprocessor">#include &lt;<a class="code" href="_kernel_helpers_8h.html">shark/Models/Kernels/KernelHelpers.h</a>&gt;</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span> </div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span> </div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span> </div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment"></span> </div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">///</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">/// \brief Training of a regularization network.</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">///</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">/// A regularization network is a kernel-based model for</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">/// regression problems. Given are data tuples</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">/// \f$ (x_i, y_i) \f$ with x-component denoting input and</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">/// y-component denoting a real-valued label (see the tutorial on</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">/// label conventions; the implementation uses RealVector),</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">/// a kernel function k(x, x&#39;) and a regularization</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">/// constant \f$ C  &gt; 0\f$. Let H denote the kernel induced</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">/// reproducing kernel Hilbert space of k, and let \f$ \phi \f$</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">/// denote the corresponding feature map.</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">/// Then the SVM regression function is of the form</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">/// \f[</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">///     f(x) = \langle w, \phi(x) \rangle + b</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment">/// \f]</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">/// with coefficients w and b given by the (primal)</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">/// optimization problem</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment">/// \f[</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment">///     \min \frac{1}{2} \|w\|^2 + C \sum_i L(y_i, f(x_i)),</span></div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="comment">/// \f]</span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment">/// where the simple quadratic loss is employed:</span></div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span><span class="comment">/// \f[</span></div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span><span class="comment">///     L(y, f(x)) = (y - f(x))^2</span></div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span><span class="comment">/// \f]</span></div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span><span class="comment">/// Regularization networks can be interpreted as a special</span></div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span><span class="comment">/// type of support vector machine (for regression, with</span></div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span><span class="comment">/// squared loss, and thus with non-sparse weights).</span></div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span><span class="comment">///</span></div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span><span class="comment">/// Training a regularization network is identical to training a</span></div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment">/// Gaussian process for regression. The parameter \f$ C \f$ then</span></div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment">/// corresponds precision of the noise (denoted by \f$ \beta \f$ in</span></div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="comment">/// Bishop&#39;s textbook). The precision is the inverse of the variance</span></div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span><span class="comment">/// of the noise. The variance of the noise is denoted by \f$</span></div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="comment">/// \sigma_n^2 \f$ in the textbook by Rasmussen and</span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span><span class="comment">/// Williams. Accordingly, \f$ C = 1/\sigma_n^2 \f$.</span></div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span><span class="comment">/// \ingroup supervised_trainer</span></div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span><span class="comment"></span><span class="keyword">template</span> &lt;<span class="keyword">class</span> InputType&gt;</div>
<div class="foldopen" id="foldopen00086" data-start="{" data-end="};">
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"><a class="line" href="classshark_1_1_regularization_network_trainer.html">   86</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_regularization_network_trainer.html" title="Training of a regularization network.">RegularizationNetworkTrainer</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html" title="Super class of all kernelized (non-linear) SVM trainers.">AbstractSvmTrainer</a>&lt;InputType, RealVector,KernelExpansion&lt;InputType&gt; &gt;</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>{</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"><a class="line" href="classshark_1_1_regularization_network_trainer.html#a681d8e1fad11bcd8a7c8a73c5e1fb38e">   89</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_model.html" title="Base class for all Models.">AbstractModel&lt;InputType, RealVector&gt;</a> <a class="code hl_typedef" href="classshark_1_1_regularization_network_trainer.html#a681d8e1fad11bcd8a7c8a73c5e1fb38e">ModelType</a>;</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"><a class="line" href="classshark_1_1_regularization_network_trainer.html#a8eda3b552bb1deb882a8fb1bcf604a89">   90</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_regularization_network_trainer.html#a8eda3b552bb1deb882a8fb1bcf604a89">KernelType</a>;</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html" title="Super class of all kernelized (non-linear) SVM trainers.">AbstractSvmTrainer&lt;InputType, RealVector, KernelExpansion&lt;InputType&gt;</a> &gt; <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html">base_type</a>;</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span><span class="comment"></span> </div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span><span class="comment">    /// \param kernel Kernel</span></div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span><span class="comment">    /// \param betaInv Inverse precision, equal to assumed noise variance, equal to inverse regularization parameter C </span></div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span><span class="comment">    /// \param unconstrained Indicates exponential encoding of the regularization parameter </span></div>
<div class="foldopen" id="foldopen00096" data-start="{" data-end="}">
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"><a class="line" href="classshark_1_1_regularization_network_trainer.html#a9893276bab3d8102d1f1d8610e7f120c">   96</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_regularization_network_trainer.html#a9893276bab3d8102d1f1d8610e7f120c">RegularizationNetworkTrainer</a>(<a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>* <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>, <span class="keywordtype">double</span> betaInv, <span class="keywordtype">bool</span> unconstrained = <span class="keyword">false</span>)</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>    : <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html">base_type</a>(<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>, 1.0 / betaInv, false, unconstrained)</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>    { }</div>
</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span><span class="comment"></span> </div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00101" data-start="{" data-end="}">
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"><a class="line" href="classshark_1_1_regularization_network_trainer.html#a9e48be55e7a79ad8a6fd1355aa2bd8da">  101</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_regularization_network_trainer.html#a9e48be55e7a79ad8a6fd1355aa2bd8da" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;RegularizationNetworkTrainer&quot;</span>; }</div>
</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span><span class="comment"></span> </div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span><span class="comment">    /// \brief Returns the assumed noise variance (i.e., 1/C) </span></div>
<div class="foldopen" id="foldopen00105" data-start="{" data-end="}">
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"><a class="line" href="classshark_1_1_regularization_network_trainer.html#a5bc0b145342c383e4b5825ba887256c7">  105</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_regularization_network_trainer.html#a5bc0b145342c383e4b5825ba887256c7" title="Returns the assumed noise variance (i.e., 1/C)">noiseVariance</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> 1.0 / this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>(); }<span class="comment"></span></div>
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<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="comment">    /// \brief Sets the assumed noise variance (i.e., 1/C) </span></div>
<div class="foldopen" id="foldopen00108" data-start="{" data-end="}">
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"><a class="line" href="classshark_1_1_regularization_network_trainer.html#a2577164dc6cc24401b2901ae9e3ac6e9">  108</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_regularization_network_trainer.html#a2577164dc6cc24401b2901ae9e3ac6e9" title="Sets the assumed noise variance (i.e., 1/C)">setNoiseVariance</a>(<span class="keywordtype">double</span> betaInv)</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>    { this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>() = 1.0 / betaInv; }</div>
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<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span><span class="comment"></span> </div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment">    /// \brief Returns the precision (i.e., C), the inverse of the assumed noise variance </span></div>
<div class="foldopen" id="foldopen00112" data-start="{" data-end="}">
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"><a class="line" href="classshark_1_1_regularization_network_trainer.html#a87ce3df17f42b18767bcb593dfbf2ac6">  112</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_regularization_network_trainer.html#a87ce3df17f42b18767bcb593dfbf2ac6" title="Returns the precision (i.e., C), the inverse of the assumed noise variance.">precision</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>(); }<span class="comment"></span></div>
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<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span><span class="comment">    /// \brief Sets the precision (i.e., C), the inverse of the assumed noise variance </span></div>
<div class="foldopen" id="foldopen00115" data-start="{" data-end="}">
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"><a class="line" href="classshark_1_1_regularization_network_trainer.html#ac4eeec2481645b35732f80dc8f96f8e5">  115</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_regularization_network_trainer.html#ac4eeec2481645b35732f80dc8f96f8e5" title="Sets the precision (i.e., C), the inverse of the assumed noise variance.">setPrecision</a>(<span class="keywordtype">double</span> beta)</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>    { this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>() = beta; }</div>
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<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span> </div>
<div class="foldopen" id="foldopen00118" data-start="{" data-end="}">
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"><a class="line" href="classshark_1_1_regularization_network_trainer.html#a0c203b749f48be1b99e679ea666ff0c0">  118</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_regularization_network_trainer.html#a0c203b749f48be1b99e679ea666ff0c0">train</a>(<a class="code hl_class" href="classshark_1_1_kernel_expansion.html" title="Linear model in a kernel feature space.">KernelExpansion&lt;InputType&gt;</a>&amp; svm, <span class="keyword">const</span> <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, RealVector&gt;</a>&amp; dataset){</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>        svm.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a38c97766f52bf00e5b0120c46c15f37f">setStructure</a>(<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>,dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>(),<span class="keyword">true</span>, <a class="code hl_function" href="group__shark__globals.html#ga3006553139477e356ee75cd85c190d7c" title="Return the label/output dimensionality of a labeled dataset.">labelDimension</a>(dataset));</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        </div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        <span class="comment">// Setup the kernel matrix</span></div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        RealMatrix M = <a class="code hl_function" href="group__kernels.html#ga3eaca71bfc1467b79c9341dcfcad25c1" title="Calculates the regularized kernel gram matrix of the points stored inside a dataset.">calculateRegularizedKernelMatrix</a>(*(this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">m_kernel</a>),dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>(), <a class="code hl_function" href="classshark_1_1_regularization_network_trainer.html#a5bc0b145342c383e4b5825ba887256c7" title="Returns the assumed noise variance (i.e., 1/C)">noiseVariance</a>());</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>        RealMatrix V = createBatch&lt;RealVector&gt;(dataset.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>());</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>        RealVector <a class="code hl_function" href="namespaceshark.html#a6ae694efca57e84792fcff090223437e" title="Calculates the mean vector of the input vectors.">mean</a> = sum(as_columns(V))/V.size1();</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>        noalias(V) -= blas::repeat(<a class="code hl_function" href="namespaceshark.html#a6ae694efca57e84792fcff090223437e" title="Calculates the mean vector of the input vectors.">mean</a>,V.size1());</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span> </div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>        <span class="comment">//check whether lambda is large enough to make the eigenvalues numerically stable</span></div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        <span class="keywordflow">if</span>(<a class="code hl_function" href="classshark_1_1_regularization_network_trainer.html#a5bc0b145342c383e4b5825ba887256c7" title="Returns the assumed noise variance (i.e., 1/C)">noiseVariance</a>()/max(diag(M)) &lt; 1.e-5)</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>            noalias(svm.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a3c65dfd17f38eaa461f6400d302fae48">alpha</a>()) = inv(M,blas::symm_semi_pos_def()) % V;</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>        <span class="keywordflow">else</span><span class="comment">//we think now it is stable so we can use the fast pure cholesky decomposition</span></div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>            noalias(svm.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a3c65dfd17f38eaa461f6400d302fae48">alpha</a>()) = inv(M,blas::symm_pos_def()) % V;</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>        noalias(svm.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a1c89cb50933ee211d67af90e6366e0ee">offset</a>()) = <a class="code hl_function" href="namespaceshark.html#a6ae694efca57e84792fcff090223437e" title="Calculates the mean vector of the input vectors.">mean</a>;</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>    }</div>
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<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>};</div>
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<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span> </div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span> </div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span><span class="comment">// A regularization network can be interpreted as a Gaussian</span></div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span><span class="comment">// process, with the same trainer:</span></div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"><a class="line" href="_regularization_network_trainer_8h.html#acdbabc51c90826fd530a53da86495b7c">  139</a></span><span class="preprocessor">#define GaussianProcessTrainer RegularizationNetworkTrainer</span></div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span> </div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span> </div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>}</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span><span class="preprocessor">#endif</span></div>
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